{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 实验四 Pandas数据预处理\n",
    "## 4.1 重复值清洗\n",
    "### 4.1.1 检测重复值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   state pop\n",
      "0      1   a\n",
      "1      1   b\n",
      "2      2   c\n",
      "3      2   d\n",
      "是否重复：\n",
      "0    False\n",
      "1    False\n",
      "2    False\n",
      "3    False\n",
      "dtype: bool\n",
      "删除state重复：\n",
      "   state pop\n",
      "0      1   a\n",
      "2      2   c\n",
      "用来判断是否更改df:\n",
      "   state pop\n",
      "0      1   a\n",
      "1      1   b\n",
      "2      2   c\n",
      "3      2   d\n",
      "删除后是否重复：\n",
      "0    False\n",
      "1     True\n",
      "2    False\n",
      "3     True\n",
      "dtype: bool\n",
      "   state pop\n",
      "0      1   a\n",
      "2      2   c\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = {'state':[1,1,2,2],'pop':['a','b','c','d']}\n",
    "df = pd.DataFrame(data)\n",
    "# duplicated()方法会检查每一行是否与之前的行完全重复，返回一个布尔序列。这里说的是此行之前的所有行，不是只有上一行，返回结果为数组\n",
    "IsDuplicated=df.duplicated()\n",
    "print(df)\n",
    "print('是否重复：')\n",
    "print(IsDuplicated)\n",
    "print('删除state重复：')\n",
    "print(df.drop_duplicates(['state']))# 保留每个state值的第一次出现，删除后续的重复值,返回值为DataFrame类型，返回删除重复后的数据\n",
    "print(\"用来判断是否更改df:\")\n",
    "print(df)\n",
    "IsDuplicated=df.duplicated(['state']) # 删除state列重复的数据行，保留数据的第一次出现\n",
    "print('删除后是否重复：')\n",
    "print(IsDuplicated)\n",
    "print(df.drop_duplicates(['state']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1.2 删除重复值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  key1 key2  data1     data2\n",
      "0    a  one      1  0.081617\n",
      "1    a  two      2 -0.420592\n",
      "2    b  one      3 -1.006779\n",
      "3    b  two      2 -0.821654\n",
      "4    a  one      1 -0.114513\n",
      "5    a  one      1 -0.080770\n",
      "基于所有列判断重复：\n",
      "0    False\n",
      "1    False\n",
      "2    False\n",
      "3    False\n",
      "4    False\n",
      "5    False\n",
      "dtype: bool\n",
      "基于key1列判断重复\n",
      "0    False\n",
      "1     True\n",
      "2    False\n",
      "3     True\n",
      "4     True\n",
      "5     True\n",
      "dtype: bool\n",
      "  key1 key2  data1     data2\n",
      "0    a  one      1  0.081617\n",
      "1    a  two      2 -0.420592\n",
      "2    b  one      3 -1.006779\n",
      "3    b  two      2 -0.821654\n",
      "4    a  one      1 -0.114513\n",
      "5    a  one      1 -0.080770\n",
      "  key1 key2  data1     data2\n",
      "0    a  one      1  0.081617\n",
      "2    b  one      3 -1.006779\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.DataFrame({'key1':['a','a','b','b','a','a'],\n",
    "                'key2':['one','two','one','two','one','one'],\n",
    "                'data1':[1,2,3,2,1,1],\n",
    "                 'data2':np.random.randn(6) # 生成服从标准正态分布（也称为高斯分布）的随机数\n",
    "                })\n",
    "print(df)\n",
    "print('基于所有列判断重复：')\n",
    "print(df.duplicated())\n",
    "print('基于key1列判断重复')\n",
    "print(df.duplicated(subset='key1')) # 判断是否存在重复\n",
    "print(df.drop_duplicates())\n",
    "print(df.drop_duplicates(subset='key1'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 缺失值清洗\n",
    "### 4.2.1 检测缺失值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       a\n",
      "1       b\n",
      "2     NaN\n",
      "3       c\n",
      "4    None\n",
      "dtype: object\n",
      "0    False\n",
      "1    False\n",
      "2     True\n",
      "3    False\n",
      "4     True\n",
      "dtype: bool\n",
      "2     NaN\n",
      "4    None\n",
      "dtype: object\n",
      "0    a\n",
      "1    b\n",
      "3    c\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "s = pd.Series([\"a\",\"b\",np.nan,\"c\",None]) # np.nan（NumPy的空值表示） None（Python的空值表示） \n",
    "print(s)\n",
    "print(s.isnull())\n",
    "print(s[s.isnull()]) # 显示所有的空值项\n",
    "print(s.dropna()) # 删除所有的空值，只保留非空值的数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2.2 删除缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0    1    2\n",
      "0  1  NaN  2.0\n",
      "1  9  NaN  NaN\n",
      "2  3  4.0  NaN\n",
      "3  5  6.0  7.0\n",
      "       0      1      2\n",
      "0  False   True  False\n",
      "1  False   True   True\n",
      "2  False  False   True\n",
      "3  False  False  False\n",
      "sum-----------\n",
      "0    0\n",
      "1    2\n",
      "2    2\n",
      "dtype: int64\n",
      "sum.sum= 4\n",
      "删除缺失值：\n",
      "删除包含空值的行\n",
      "   0    1    2\n",
      "3  5  6.0  7.0\n",
      "删除包含空值的列\n",
      "   0\n",
      "0  1\n",
      "1  9\n",
      "2  3\n",
      "3  5\n",
      "   0    1    2\n",
      "0  1  NaN  2.0\n",
      "1  9  NaN  NaN\n",
      "2  3  4.0  NaN\n",
      "3  5  6.0  7.0\n",
      "     0    1    2\n",
      "0  1.0  NaN  2.0\n",
      "1  NaN  NaN  NaN\n",
      "2  3.0  4.0  NaN\n",
      "3  5.0  6.0  7.0\n",
      "     0    1    2\n",
      "0  1.0  NaN  2.0\n",
      "2  3.0  4.0  NaN\n",
      "3  5.0  6.0  7.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "a = [[1, np.nan, 2],[9,None,np.nan],[3, 4, None],[5,6,7]]\n",
    "df = pd.DataFrame(a)\n",
    "print(df)\n",
    "print(df.isnull())\n",
    "print('sum-----------')\n",
    "print(df.isnull().sum()) # 统计每列的空值数量\n",
    "print('sum.sum=',df.isnull().sum().sum()) # 统计整个DataFrame中的空值总数\n",
    "print('删除缺失值：')\n",
    "print(\"删除包含空值的行\")\n",
    "print(df.dropna()) # 删除包含空值的行\n",
    "print(\"删除包含空值的列\")\n",
    "print(df.dropna(axis=1)) # 删除包含空值的列\n",
    "print(df.dropna(how=\"all\"))# 删除全为空值的行：\n",
    "df.at[1,0] = None\n",
    "print(df)\n",
    "print(df.dropna(how='all'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2.3 填充法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     A    B   C  D\n",
      "0  NaN  2.0 NaN  0\n",
      "1  3.0  4.0 NaN  1\n",
      "2  NaN  NaN NaN  5\n",
      "3  NaN  3.0 NaN  4\n",
      "     A    B    C  D\n",
      "0  0.0  2.0  0.0  0\n",
      "1  3.0  4.0  0.0  1\n",
      "2  0.0  0.0  0.0  5\n",
      "3  0.0  3.0  0.0  4\n",
      "     A    B   C  D\n",
      "0  NaN  2.0 NaN  0\n",
      "1  3.0  4.0 NaN  1\n",
      "2  3.0  4.0 NaN  5\n",
      "3  3.0  3.0 NaN  4\n",
      "     A    B   C  D\n",
      "0  3.0  2.0 NaN  0\n",
      "1  3.0  4.0 NaN  1\n",
      "2  NaN  3.0 NaN  5\n",
      "3  NaN  3.0 NaN  4\n",
      "     A    B    C  D\n",
      "0  0.0  2.0  2.0  0\n",
      "1  3.0  4.0  2.0  1\n",
      "2  0.0  1.0  2.0  5\n",
      "3  0.0  3.0  2.0  4\n",
      "     A    B    C  D\n",
      "0  0.0  2.0  2.0  0\n",
      "1  3.0  4.0  NaN  1\n",
      "2  NaN  1.0  NaN  5\n",
      "3  NaN  3.0  NaN  4\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.DataFrame([ [np.nan,2,np.nan,0],\n",
    "                    [3,4,np.nan,1],\n",
    "                    [np.nan,np.nan,np.nan,5],\n",
    "                    [np.nan,3,np.nan,4]],\n",
    "                    columns=list('ABCD'))\n",
    "print(df) \n",
    "print(df.fillna(0)) # 使用固定值填充空值\n",
    "print(df.ffill()) # 前向填充,用前面的有效值来填充后面的空值,按列填充,前面若没有有效值，则保持空\n",
    "print(df.bfill()) # 后向填充\n",
    "values = {'A': 0, 'B': 1, 'C': 2, 'D': 3} \n",
    "print(df.fillna(value=values)) # 为每一列指定不同的填充值\n",
    "print(df.fillna(value=values, limit=1)) # 多个连续的空值，只填充第一个"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2.4 插值法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 暂时看不懂\n",
    "import numpy as np\n",
    "from scipy import interpolate\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# 创建了4个已知的数据点\n",
    "# 这些点将作为插值的基准点\n",
    "x=np.array([-1, 0, 2.0, 1.0])\n",
    "y=np.array([1.0, 0.3, -0.5, 0.8])\n",
    "xnew=np.linspace(-3,4,100) # 在[-3,4]范围内生成100个均匀分布的点，这些点将用于生成平滑的插值曲线\n",
    "# print(xnew)\n",
    "for kind in['multiquadric','gaussian','linear']:\n",
    "    # 根据已经给出的x,y的基准点，选择径向基函数，会生成对应y坐标值，使得生成的图形曲线光滑\n",
    "    ff=interpolate.Rbf(x,y,kind=kind)\n",
    "    ynew=ff(xnew) # 这行和上一行是核心\n",
    "    # plt.figure() # 放在这里和最后防止plt.show()的效果差不多，figure会自动创建新窗口，show则是自动创建和关闭\n",
    "    plt.plot(x,y,'ro') # 'ro'表示红色圆点\n",
    "    plt.plot(xnew,ynew,label=str(kind))\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.show()\n",
    "    # 1. 显示当前图形\n",
    "    # 2. 关闭当前图形\n",
    "    # 3. 为下一次绘图创建新的图形\n",
    "# 数据特征平滑时选择multiquadric\n",
    "# 需要局部特征保持时选择gaussian\n",
    "# 计算速度要求高时选择linear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 检测异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 0            1            2            3\n",
      "count  1000.000000  1000.000000  1000.000000  1000.000000\n",
      "mean     -0.016329     0.025399    -0.058489    -0.035284\n",
      "std       1.021317     0.998635     0.986187     1.010868\n",
      "min      -4.446057    -2.862940    -3.193882    -3.650892\n",
      "25%      -0.741818    -0.630558    -0.722050    -0.743380\n",
      "50%      -0.056331     0.024547    -0.063468    -0.051106\n",
      "75%       0.651067     0.692565     0.634930     0.681768\n",
      "max       3.909863     3.130972     3.388521     3.252798\n",
      "找出某一列中绝对值大小超过3的项\n",
      "420    3.252798\n",
      "575   -3.650892\n",
      "725   -3.036076\n",
      "Name: 3, dtype: float64\n",
      "找出全部绝对值超过3的值的行\n",
      "125    1.697089\n",
      "192    0.948863\n",
      "420    3.252798\n",
      "451   -0.128132\n",
      "491    1.238875\n",
      "575   -3.650892\n",
      "686   -0.356281\n",
      "687   -0.363465\n",
      "725   -3.036076\n",
      "836   -0.582005\n",
      "861   -0.070612\n",
      "990    1.336684\n",
      "Name: 3, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nkpOTjcvlMj/96U9jtv/+pPvvv994vV4jKWZb9AkTJrS6qVfzplvGfLiR1/Tp082YMWPMRRddZLZv396i/zNnzhhJ5re//W20bdiwYebZZ5+Nua+6utpIMv/zP/8TbXv00UfN66+/bpqamqJtixYtMv379zeDBg0yL7/8sjHGmJ07d5pLL73UDB061PTs2dPcf//90fuHDBliJH2uY968eef+ywXQKsIG0MFs2LDBFBYWRncFbRYIBIwkc+TIkfPq/7333jMTJ040gUDgM+/duHGjuemmm8wjjzxiQqFQtP0Pf/iDeffdd6Pnp0+fNoFAwHzwwQfRtrKyMnPTTTeZRYsWmd27d7fafyQSMfv374/53Pl48cUXzYMPPmhqa2tj2vfu3Wtuuukm881vftOcOnUq2u7xeGLejfJZJk2aFBNWALRNgjGf8oAUALqwgwcPKjk5+ZwrfAA4g7ABAACsYoIoAACwirABAACsImwAAACrCBsAAMCqDrGDaCQS0eHDh9WnTx+2BgYAoJMwxqi+vl6DBg1SYuK5xy86RNg4fPiwUlNT410GAAD4Aqqrq6MvZmxNhwgbzWvcq6urY17WBAAAOq66ujqlpqZ+5l41HSJsND866du3L2EDAIBO5rOmQDBBFAAAWEXYAAAAVhE2AACAVYQNAABgFWEDAABYZSVsTJ48WWvXrrXRNQAA6GQcDxvPP/+8Nm7c6HS3AACgk3I0bBw/fly5ubkaNmyYk90CAIBOzNFNvXJzc/Wtb31LjY2NTnYLoJMKh8MKBAKqqalRSkqKMjIy5HK54l0WgHbm2MhGWVmZNm3apJ///OefeW8oFFJdXV3MAaBrKS4ultfrVWZmpnJycpSZmSmv16vi4uJ4lwagnTkSNk6fPq17771Xq1atatN24/n5+UpOTo4evIQN6FqKi4uVnZ0tn8+n8vJy1dfXq7y8XD6fT9nZ2QQO4AKTYIwx59vJwoULVVVVpeeff16SNGPGDE2cOFEzZsxo9f5QKKRQKBQ9b36RS21tLe9GATq5cDgsr9crn8+nkpKSmNdORyIR+f1+BYNBVVZW8kgF6OTq6uqUnJz8mb/fjoQNj8ejo0ePyu3+cApIQ0OD3G63ZsyYoZUrVzpWLICO77XXXlNmZqbKy8s1fvz4FtfLy8uVnp6usrIyTZw4sf0LBOCYtv5+OzJBNBAIqKmpKXo+d+5cjR8//pwjGwC6rpqaGknSyJEjW73e3N58H4Cuz5GwccUVV8Sc9+7dWwMHDtTAgQOd6B5AJ5KSkiJJCgaDrY5sBIPBmPsAdH2OPEY5XzxGAboO5mwAF462/n7zbhQAjnK5XCosLFRpaan8fn/MahS/36/S0lIVFBQQNIALiKObegGAJGVlZamoqEi5ublKT0+Ptns8HhUVFSkrKyuO1QFobzxGAWANO4gCXVu7rkYBgNa4XC6WtwJgzgYAALCLsAEAAKwibAAAAKsIGwAAwCrCBgAAsIqwAQAArCJsAAAAqwgbAADAKjb1AmANO4gCkBjZAGBJcXGxvF6vMjMzlZOTo8zMTHm9XhUXF8e7NADtjLABwHHFxcXKzs6Wz+eLeeurz+dTdnY2gQO4wPAiNgCOCofD8nq98vl8KikpUWLiR/+miUQi8vv9CgaDqqys5JEK0Mm19febkQ0AjgoEAqqqqtKCBQtigoYkJSYmKi8vT/v371cgEIhThQDaG2EDgKNqamokSSNHjmz1enN7830Auj7CBgBHpaSkSJKCwWCr15vbm+8D0PURNgA4KiMjQ2lpaVq2bJkikUjMtUgkovz8fHk8HmVkZMSpQgDtjbABwFEul0uFhYUqLS2V3++PWY3i9/tVWlqqgoICJocCFxA29QLguKysLBUVFSk3N1fp6enRdo/Ho6KiImVlZcWxOgDtjaWvAKxhB1Gga2vr7zcjGwCscblcmjhxYrzLABBnzNkAAABWETYAAIBVhA0AAGAVYQMAAFhF2AAAAFaxGgWANSx9BSAxsgHAkuLiYnm9XmVmZionJ0eZmZnyer0qLi6Od2kA2hlhA4DjiouLlZ2dLZ/PF7Nduc/nU3Z2NoEDuMCwgygAR4XDYXm9Xvl8PpWUlCgx8aN/00QiEfn9fgWDQVVWVvJIBejk2vr7zcgGAEcFAgFVVVVpwYIFMUFDkhITE5WXl6f9+/crEAjEqUIA7Y2wAcBRNTU1kqSRI0e2er25vfk+AF0fYQOAo1JSUiRJwWCw1evN7c33Aej6CBsAHJWRkaG0tDQtW7ZMkUgk5lokElF+fr48Ho8yMjLiVCGA9kbYAOAol8ulwsJClZaWyu/3x6xG8fv9Ki0tVUFBAZNDgQsIm3oBcFxWVpaKioqUm5ur9PT0aLvH41FRUZGysrLiWB2A9sbSVwDWnDlzRitXrtTevXv1la98RbNnz1b37t3jXRYAh7T195uRDQBWFBcXKzc3V1VVVdG2xx57TIWFhYxsABcY5mwAcBw7iAL4OB6jAHAUO4gCFw52EAUQF+wgCuCTCBsAHMUOogA+iQmiAFpoaGjQzp07v9Bn6+vrJUnr16+Xz+dTY2OjqqqqlJaWph49emj79u3R+7Zt2/a5+x8+fLh69uz5hWoDEB/M2QDQwrZt2zR69Oh4l9GqrVu3atSoUfEuA4BY+grgPAwfPlxbt279wp9/9dVXNX/+fGVkZCgzM1NLlizR4sWLVVZWpkAgoBUrVuiGG274wrUB6FwY2QBgRWv7bHg8HhUUFLDPBtBFsBoFQFxlZWVpz549Wr16tSRp9erVqqysJGgAFyDCBgBrXC6XxowZI0kaM2YM+2oAFyjCBgAAsIqwAQAArCJsAAAAqwgbAADAKsIGAACwirABAACsImwAAACrCBsAAMAqwgYAALCKsAEAAKwibAAAAKsIGwAAwCrCBgAAsIqwAQAArCJsAAAAqwgbAADAKsIGAACwirABAACsImwAAACrHAsbL774ogYPHiy3261x48apoqLCqa4BAEAn5kjY2Lt3r+6++24tX75chw4d0pe//GXNnDnTia4BAEAn53aik4qKCi1btkzf+c53JEk/+MEPNHnyZCe6BgAAnZwjYWPq1Kkx57t27ZLX6z3n/aFQSKFQKHpeV1fnRBkAAKADcnyC6JkzZ1RQUKDZs2ef8578/HwlJydHj9TUVKfLAAAAHYTjYeOhhx5S79699b3vfe+c9+Tl5am2tjZ6VFdXO10GAADoIBx5jNLslVde0ZNPPqk333xT3bp1O+d9SUlJSkpKcvKrAQBAB+XYyMa+fft05513atWqVbrqqquc6hYAAHRyjoxsNDY2aurUqfL7/br11lt16tQpSVKvXr2UkJDgxFcAAIBOypGRjY0bN6qiokJPP/20+vTpEz0OHDjgRPcAAKATc2Rkw+/3yxjjRFcAAKCL4d0oAADAKsIGAACwirABAACsImwAAACrCBsAAMAqwgYAALCKsAEAAKwibAAAAKsIGwAAwCrCBgAAsIqwAQAArCJsAAAAqwgbAADAKsIGAACwirABAACsImwAAACrCBsAAMAqwgYAALCKsAEAAKwibAAAAKsIGwAAwCrCBgAAsIqwAQAArCJsAAAAqwgbAADAKsIGAACwirABAACsImwAAACrCBsAAMAqwgYAALCKsAEAAKwibAAAAKsIGwAAwCrCBgAAsIqwAQAArCJsAAAAqwgbAADAKsIGAACwirABAACsImwAAACrCBsAAMAqwgYAALCKsAEAAKwibAAAAKsIGwAAwCrCBgAAsIqwAQAArHLHuwAAzqqsrFR9fX28y4iqqKiI+d+Ook+fPhoyZEi8ywAuCIQNoAuprKzU0KFD411Gq6ZNmxbvElrYvXs3gQNoB4QNoAtpHtFYt26dRowYEedqPtTY2KiqqiqlpaWpR48e8S5H0oejLNOmTetQI0BAV0bYALqgESNGaNSoUfEuI+raa6+NdwkA4ogJogAAwCrCBgAAsIqwAQAArCJsAAAAqwgbAADAKsIGAACwirABAACsImwAAACrCBsAAMAqwgYAALCKsAEAAKwibAAAAKsIGwAAwCrCBgAAsIqwAQAArCJsAAAAqwgbAADAKsIGAACwirABAACscixsBINBjR07Vv3799e8efNkjHGqawAA0Ik5EjZCoZBuvvlmjR49Wu+884527NihtWvXOtE1AADo5BwJG3/4wx9UW1urX/ziF/rKV76iZcuW6ZlnnnGiawAA0Mm5nejk3Xff1fjx49WzZ09J0tVXX60dO3ac8/5QKKRQKBQ9r6urc6IMAADQATkyslFXVyePxxM9T0hIkMvl0okTJ1q9Pz8/X8nJydEjNTXViTIAAEAH5MjIhtvtVlJSUkzbRRddpIaGBvXv37/F/Xl5eXrggQei53V1dQQOwCGX9U5Qj5O7pcMsNjuXHid367LeCfEuA7hgOBI2BgwYoGAwGNNWX1+v7t27t3p/UlJSi3ACwBn3ju6uEa/fK70e70o6rhH68O8JQPtwJGyMHTtWa9asiZ5XVVUpFAppwIABTnQP4HNYvfWMblu0ViOGD493KR1Wxc6dWl2Yo1viXQhwgXAkbFx//fWqra3Vr3/9a02fPl3Lly/XjTfeKJfL5UT3AD6HI6eMGvsNlQZ9Nd6ldFiNRyI6coq9gID24ticjaeeeko5OTmaN2+ewuGwNm/e7ETXAACgk3MkbEiS3+9XZWWl3nnnHaWnp+uSSy5xqmsAANCJORY2JOnyyy/X5Zdf7mSXAACgk2NtHAAAsIqwAQAArCJsAAAAqwgbAADAKsIGAACwirABAACsImwAAACrCBsAAMAqwgYAALCKsAEAAKwibAAAAKsIGwAAwCrCBgAAsIqwAQAArCJsAAAAqwgbAADAKsIGAACwirABAACsImwAAACrCBsAAMAqwgYAALDKHe8CADinoaFBkrRt27Y4V/KRxsZGVVVVKS0tTT169Ih3OZKkioqKeJcAXFAIG0AXsnPnTknSrFmz4lxJ59CnT594lwBcEAgbQBfi9/slScOHD1fPnj3jW8z/V1FRoWnTpmndunUaMWJEvMuJ6tOnj4YMGRLvMoALAmED6EIGDhyomTNnxruMVo0YMUKjRo2KdxkA4oAJogAAwCrCBgAAsIqwAQAArCJsAAAAqwgbAADAKsIGAACwirABAACsImwAAACrCBsAAMAqwgYAALCKsAEAAKwibAAAAKsIGwAAwCrCBgAAsIqwAQAArCJsAAAAqwgbAADAKsIGAACwirABAACsImwAAACrCBsAAMAqwgYAALCKsAEAAKwibAAAAKsIGwAAwCrCBgAAsIqwAQAArCJsAAAAqwgbAADAKsIGAACwirABAACsImwAAACrCBsAAMAqwgYAALCKsAEAAKwibAAAAKsIGwAAwCrCBgAAsIqwAQAArCJsAAAAqwgbAADAKsIGAACwirABAACsciRsvPjiixo8eLDcbrfGjRuniooKJ7oFAABdwHmHjb179+ruu+/W8uXLdejQIX35y1/WzJkznagNAAB0Ae7z7aCiokLLli3Td77zHUnSD37wA02ePPm8CwMAAF3DeYeNqVOnxpzv2rVLXq/3Uz8TCoUUCoWi53V1dedbBgAA6KDa/BjF7/erX79+LY4nnngies+ZM2dUUFCg2bNnf2pf+fn5Sk5Ojh6pqalf/E8AAAA6tDaPbKxevVqNjY0t2gcMGBD974ceeki9e/fW9773vU/tKy8vTw888ED0vK6ujsABAEAX1eaw8aUvfelTr7/yyit68skn9eabb6pbt26fem9SUpKSkpLa+tUAAKATc2Tp6759+3TnnXdq1apVuuqqq5zoEgAAdBHnPUG0sbFRU6dOld/v16233qpTp05Jknr16qWEhITzLhAAAHRu5z2ysXHjRlVUVOjpp59Wnz59oseBAwecqA8AAHRy5z2y4ff7ZYxxohYAANAF8W4UAABgFWEDAABYRdgAAABWETYAAIBVhA0AAGAVYQMAAFhF2AAAAFYRNgAAgFWEDQAAYBVhAwAAWEXYAAAAVhE2AACAVYQNAABgFWEDAABYRdgAAABWETYAAIBVhA0AAGAVYQMAAFhF2AAAAFYRNgAAgFWEDQAAYBVhAwAAWEXYAAAAVhE2AACAVe54FwCg42loaNDOnTvPu59wOKySkhJJ0vr16xUOh+Vyuc6rz+HDh6tnz57nXRuA9pNgjDHxLqKurk7Jycmqra1V3759410OcMHbtm2bRo8eHe8yWrV161aNGjUq3mUAUNt/vxnZANDC8OHDtXXr1i/8+VdffVXz589XRkaGcnJy5Ha71dTUpN/85jcKBAJasWKFbrjhhi9cG4DOhZENAI4Kh8Pyer3y+XwqKSlRYuJHU8MikYj8fr+CwaAqKyvP+5EKgPhq6+83E0QBOCoQCKiqqkoLFiyICRqSlJiYqLy8PO3fv1+BQCBOFQJob4QNAI6qqamRJI0cObLV683tzfcB6PoIGwAclZKSIkkKBoOtXm9ub74PQNdH2ADgqIyMDKWlpWnZsmWKRCIx1yKRiPLz8+XxeJSRkRGnCgG0N8IGAEe5XC4VFhaqtLRUfr9f5eXlqq+vV3l5ufx+v0pLS1VQUMDkUOACwtJXAI7LyspSUVGRcnNzlZ6eHm33eDwqKipSVlZWHKsD0N5Y+grAmnA4rEAgoJqaGqWkpCgjI4MRDaALYVMvAHHncrk0ceLEeJcBIM6YswEAAKwibAAAAKsIGwAAwCrCBgAAsIqwAQAArCJsAAAAqwgbAADAKsIGAACwirABAACsImwAAACrCBsAAMAqwgYAALCKsAEAAKwibAAAAKsIGwAAwCp3vAsA0HWFw2EFAgHV1NQoJSVFGRkZcrlc8S4LQDtjZAOAFcXFxfJ6vcrMzFROTo4yMzPl9XpVXFwc79IAtDPCBgDHFRcXKzs7Wz6fT+Xl5aqvr1d5ebl8Pp+ys7MJHMAFJsEYY+JdRF1dnZKTk1VbW6u+ffvGuxwA5yEcDsvr9crn86mkpESJiR/9myYSicjv9ysYDKqyspJHKkAn19bfb0Y2ADgqEAioqqpKCxYsiAkakpSYmKi8vDzt379fgUAgThUCaG+EDQCOqqmpkSSNHDmy1evN7c33Aej6CBsAHJWSkiJJCgaDrV5vbm++D0DXR9gA4KiMjAylpaVp2bJlikQiMdcikYjy8/Pl8XiUkZERpwoBtDfCBgBHuVwuFRYWqrS0VH6/P2Y1it/vV2lpqQoKCpgcClxA2NQLgOOysrJUVFSk3NxcpaenR9s9Ho+KioqUlZUVx+oAtDeWvgKwhh1Ega6trb/fjGwAsMblcmnixInxLgNAnDFnAwAAWEXYAAAAVhE2AACAVYQNAABgFWEDAABYRdgAAABWOR42Jk+erLVr1zrdLQAA6KQcDRvPP/+8Nm7c6GSXAACgk3MsbBw/fly5ubkaNmyYU10CAIAuwLEdRHNzc/Wtb31LjY2NTnUJAAC6gDaHDb/fr9dee61F+8MPP6y/+qu/0qZNmxQMBvXDH/7wM/sKhUIKhULR87q6uraWAQAAOpk2h43Vq1e3OmoxYMAAjRkzRqtWrWrzS9Ty8/O1ZMmStlcJAAA6rTaHjS996Uutti9cuFBjx47VN7/5zTZ/aV5enh544IHoeV1dnVJTU9v8eQAA0Hmc9yvmPR6Pjh49Krf7w9zS0NAgt9utGTNmaOXKlW3qg1fMAwDQ+bTbK+YDgYCampqi53PnztX48eM1Y8aM8+0aAAB0AecdNq644oqY8969e2vgwIEaOHDg+XYNAAC6AMeWvjZj91AAAPBxvBsFAABYRdgAAABWETYAAIBVhA0AAGAVYQMAAFhF2AAAAFY5vvQVAJqFw2EFAgHV1NQoJSVFGRkZcrlc8S4LQDtjZAOAFcXFxfJ6vcrMzFROTo4yMzPl9XpVXFwc79IAtDPCBgDHFRcXKzs7Wz6fT+Xl5aqvr1d5ebl8Pp+ys7MJHMAF5rxfxOYEXsQGdB3hcFher1c+n08lJSVKTPzo3zSRSER+v1/BYFCVlZU8UgE6ubb+fjOyAcBRgUBAVVVVWrBgQUzQkKTExETl5eVp//79CgQCcaoQQHsjbABwVE1NjSRp5MiRrV5vbm++D0DXR9gA4KiUlBRJUjAYbPV6c3vzfQC6PsIGAEdlZGQoLS1Ny5YtUyQSibkWiUSUn58vj8ejjIyMOFUIoL0RNgA4yuVyqbCwUKWlpfL7/TGrUfx+v0pLS1VQUMDkUOACwqZeAByXlZWloqIi5ebmKj09Pdru8XhUVFSkrKysOFYHoL2x9BWANewgCnRtbf39ZmQDgDUul0sTJ06MdxkA4ow5GwAAwCrCBgAAsIqwAQAArCJsAAAAqwgbAADAKsIGAACwirABAACsImwAAACrCBsAAMCqDrGDaPOO6XV1dXGuBAAAtFXz7/ZnvfmkQ4SN+vp6SVJqamqcKwEAAJ9XfX29kpOTz3m9Q7yILRKJ6PDhw+rTp48SEhLiXQ4AB9XV1Sk1NVXV1dW8aBHoYowxqq+v16BBg5SYeO6ZGR0ibADounirMwAmiAIAAKsIGwAAwCrCBgCrkpKStHjxYiUlJcW7FABxwpwNAABgFSMbAADAKsIGAACwirABAACsImwAsObYsWPyeDyqqqqKdykA4oiwAcCKv/zlL5o6dSpBAwBhA4Adt99+u26//fZ4lwGgA2DpKwAr9u3bp8GDByshIUH79+9XWlpavEsCECeMbACwYvDgwfEuAUAHQdgAAABWETYAAIBVhA0AAGAVYQMAAFhF2AAAAFa5410AgK6N1fUAGNkAAABWETYAAIBVhA0AAGAVYQMAAFhF2AAAAFYRNgAAgFWEDQAAYBVhAwAAWEXYAAAAVhE2AACAVYQNAABg1f8DKY2eTjV4qLkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from pandas import Series, DataFrame\n",
    "from numpy import nan as NA\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# 设置中文字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False     # 用来正常显示负号\n",
    "\n",
    "df=DataFrame(np.random.randn(1000,4)) # 1000行4列的DataFrame\n",
    "# print(df)\n",
    "# matplotlib自动为第一个图创建figure\n",
    "plt.scatter(df[0],df[1]) # 散点图\n",
    "plt.figure() # 创建新的图形\n",
    "# 箱线图直观的显示出三个四分位数，分别为 Q3=75%，Q2=50% ，Q1=25%在图中从上到下为Q3-2-1\n",
    "plt.boxplot(df[0]) # 箱线图,显示第0列数据的统计分布\n",
    "plt.title('第0列的箱线图')  # 添加标题便于区分\n",
    "# plt.figure()\n",
    "# plt.boxplot(df) # 画出所有数据的箱线图\n",
    "plt.figure()\n",
    "plt.hist(df[1]) # 直方图\n",
    "plt.title('第1列的直方图') \n",
    "print(df.describe())\n",
    "print(\"找出某一列中绝对值大小超过3的项\")\n",
    "col=df[3]\n",
    "print(col[np.abs(col) > 3] )\n",
    "print(\"找出全部绝对值超过3的值的行\")\n",
    "print(col[(np.abs(df) > 3).any(axis=1)] ) # 找出任意列中有 >3 的数据的行，就是说先找到有 > 3的数据，然后统计数据所在行，并把col中对应数据打印"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.4 merge合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  key  data1\n",
      "0   b      0\n",
      "1   b      1\n",
      "2   a      2\n",
      "3   a      3\n",
      "4   b      4\n",
      "5   a      5\n",
      "6   c      6\n",
      "  key  data2\n",
      "0   a      0\n",
      "1   b      1\n",
      "2   d      2\n",
      "merge(key)=\n",
      "  key  data1  data2\n",
      "0   b      0      1\n",
      "1   b      1      1\n",
      "2   a      2      0\n",
      "3   a      3      0\n",
      "4   b      4      1\n",
      "5   a      5      0\n",
      "左连接=\n",
      "  key  data1  data2\n",
      "0   b      0    1.0\n",
      "1   b      1    1.0\n",
      "2   a      2    0.0\n",
      "3   a      3    0.0\n",
      "4   b      4    1.0\n",
      "5   a      5    0.0\n",
      "6   c      6    NaN\n",
      "基于key1,key2两个键合并\n",
      "  key1 key2  data1  data2\n",
      "0    b    j      1      1\n",
      "1    a    k      2      0\n",
      "2    a    k      3      0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df1=pd.DataFrame({'key':['b','b','a','a','b','a','c'],'data1':range(7)})\n",
    "df2=pd.DataFrame({'key':['a','b','d'],'data2':range(3)})\n",
    "print(df1)\n",
    "print(df2)\n",
    "print('merge(key)=')\n",
    "print(pd.merge(df1,df2,on='key')) # 内连接，结果只包含'a'和'b'的记录，二者皆有的key\n",
    "print('左连接=')\n",
    "print(pd.merge(df1,df2,how='left')) # 保留左边DataFrame(df1)的所有记录，如果右边DataFrame(df2)中没有匹配的key，则填充NaN\n",
    "df3 = pd.DataFrame({'key1':['b','b','a','a','b','a','c'],'key2':['i','j','k','k','i','j','k'],'data1':range(7)})\n",
    "df4 = pd.DataFrame({'key1':['a','b','d'],'key2':['k','j','i'],'data2':range(3)})\n",
    "print('基于key1,key2两个键合并')\n",
    "print(pd.merge(df3,df4,on=['key1','key2']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.5 concat合并\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      A    B    C    D\n",
      "0    A0   B0   C0   D0\n",
      "1    A1   B1   C1   D1\n",
      "2    A2   B2   C2   D2\n",
      "3    A3   B3   C3   D3\n",
      "4    A4   B4   C4   D4\n",
      "5    A5   B5   C5   D5\n",
      "6    A6   B6   C6   D6\n",
      "7    A7   B7   C7   D7\n",
      "8    A8   B8   C8   D8\n",
      "9    A9   B9   C9   D9\n",
      "10  A10  B10  C10  D10\n",
      "11  A11  B11  C11  D11\n",
      "      A    B    C    D    A    B    C    D    A    B    C    D\n",
      "0    A0   B0   C0   D0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
      "1    A1   B1   C1   D1  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
      "2    A2   B2   C2   D2  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
      "3    A3   B3   C3   D3  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
      "4   NaN  NaN  NaN  NaN   A4   B4   C4   D4  NaN  NaN  NaN  NaN\n",
      "5   NaN  NaN  NaN  NaN   A5   B5   C5   D5  NaN  NaN  NaN  NaN\n",
      "6   NaN  NaN  NaN  NaN   A6   B6   C6   D6  NaN  NaN  NaN  NaN\n",
      "7   NaN  NaN  NaN  NaN   A7   B7   C7   D7  NaN  NaN  NaN  NaN\n",
      "8   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A8   B8   C8   D8\n",
      "9   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A9   B9   C9   D9\n",
      "10  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A10  B10  C10  D10\n",
      "11  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A11  B11  C11  D11\n",
      "        A    B    C    D\n",
      "x 0    A0   B0   C0   D0\n",
      "  1    A1   B1   C1   D1\n",
      "  2    A2   B2   C2   D2\n",
      "  3    A3   B3   C3   D3\n",
      "y 4    A4   B4   C4   D4\n",
      "  5    A5   B5   C5   D5\n",
      "  6    A6   B6   C6   D6\n",
      "  7    A7   B7   C7   D7\n",
      "z 8    A8   B8   C8   D8\n",
      "  9    A9   B9   C9   D9\n",
      "  10  A10  B10  C10  D10\n",
      "  11  A11  B11  C11  D11\n",
      "      x                   y                   z               \n",
      "      A    B    C    D    A    B    C    D    A    B    C    D\n",
      "0    A0   B0   C0   D0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
      "1    A1   B1   C1   D1  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
      "2    A2   B2   C2   D2  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
      "3    A3   B3   C3   D3  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
      "4   NaN  NaN  NaN  NaN   A4   B4   C4   D4  NaN  NaN  NaN  NaN\n",
      "5   NaN  NaN  NaN  NaN   A5   B5   C5   D5  NaN  NaN  NaN  NaN\n",
      "6   NaN  NaN  NaN  NaN   A6   B6   C6   D6  NaN  NaN  NaN  NaN\n",
      "7   NaN  NaN  NaN  NaN   A7   B7   C7   D7  NaN  NaN  NaN  NaN\n",
      "8   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A8   B8   C8   D8\n",
      "9   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A9   B9   C9   D9\n",
      "10  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A10  B10  C10  D10\n",
      "11  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  A11  B11  C11  D11\n",
      "    A   B   C   D\n",
      "0  A0  B0  C0  D0\n",
      "1  A1  B1  C1  D1\n",
      "2  A2  B2  C2  D2\n",
      "3  A3  B3  C3  D3\n",
      "4  A4  B4  C4  D4\n",
      "5  A5  B5  C5  D5\n",
      "6  A6  B6  C6  D6\n",
      "7  A7  B7  C7  D7\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],\n",
    "                     'B': ['B0', 'B1', 'B2', 'B3'],\n",
    "                     'C': ['C0', 'C1', 'C2', 'C3'],\n",
    "                     'D': ['D0', 'D1', 'D2', 'D3']},\n",
    "                    index=[0, 1, 2, 3])\n",
    "df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],\n",
    "                    'B': ['B4', 'B5', 'B6', 'B7'],\n",
    "                    'C': ['C4', 'C5', 'C6', 'C7'],\n",
    "                    'D': ['D4', 'D5', 'D6', 'D7']},\n",
    "                   index=[4,5,6,7])\n",
    "df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'],\n",
    "                    'B': ['B8', 'B9', 'B10', 'B11'],\n",
    "                    'C': ['C8', 'C9', 'C10', 'C11'],\n",
    "                    'D': ['D8', 'D9', 'D10', 'D11']},\n",
    "                   index=[8, 9, 10, 11]) \n",
    "df = [df1, df2, df3] \n",
    "# print(df)\n",
    "result = pd.concat(df)\n",
    "print(result)\n",
    "#  print(df1.appand([df2, df3]))\n",
    "print(pd.concat(df, axis=1))\n",
    "print(pd.concat(df, keys=['x', 'y', 'z']))\n",
    "print(pd.concat(df, keys=['x', 'y', 'z'], axis=1))\n",
    "print(pd.concat([df1, df2], join='inner'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6 combine_first合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     a    b   c\n",
      "0  1.0  NaN   2\n",
      "1  NaN  2.0   6\n",
      "2  5.0  NaN  10\n",
      "3  NaN  6.0  14\n",
      "     a    b\n",
      "0  5.0  NaN\n",
      "1  4.0  3.0\n",
      "2  NaN  4.0\n",
      "3  3.0  6.0\n",
      "4  7.0  8.0\n",
      "     a    b     c\n",
      "0  1.0  NaN   2.0\n",
      "1  4.0  2.0   6.0\n",
      "2  5.0  4.0  10.0\n",
      "3  3.0  6.0  14.0\n",
      "4  7.0  8.0   NaN\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df1 = pd.DataFrame({'a': [1., np.nan, 5., np.nan],\n",
    "                 'b': [np.nan, 2., np.nan, 6.],\n",
    "                 'c': range(2, 18, 4)})\n",
    "df2 = pd.DataFrame({'a': [5., 4., np.nan, 3., 7.],\n",
    "                 'b': [np.nan, 3., 4., 6., 8.]})\n",
    "print(df1)\n",
    "print(df2)\n",
    "df=df1.combine_first(df2)\n",
    "print(df)\n",
    "# 优先使用df1中的非空值\n",
    "# 当df1中有空值（NaN）时，使用df2中对应位置的值来填充\n",
    "# 如果两个DataFrame都有的列，优先用df1的值\n",
    "# 如果某列只在一个DataFrame中存在，保留该列"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## 4.7 数据重塑"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   水果  数量    价格\n",
      "0  黄桃  13   2.5\n",
      "1  香蕉  40   2.0\n",
      "2  西瓜  63  11.0\n",
      "3  草莓  25   5.8\n",
      "0  水果      黄桃\n",
      "   数量      13\n",
      "   价格     2.5\n",
      "1  水果      香蕉\n",
      "   数量      40\n",
      "   价格     2.0\n",
      "2  水果      西瓜\n",
      "   数量      63\n",
      "   价格    11.0\n",
      "3  水果      草莓\n",
      "   数量      25\n",
      "   价格     5.8\n",
      "dtype: object\n",
      "   水果  数量    价格\n",
      "0  黄桃  13   2.5\n",
      "1  香蕉  40   2.0\n",
      "2  西瓜  63  11.0\n",
      "3  草莓  25   5.8\n",
      "      0    1     2    3\n",
      "水果   黄桃   香蕉    西瓜   草莓\n",
      "数量   13   40    63   25\n",
      "价格  2.5  2.0  11.0  5.8\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "df = pd.DataFrame({'水果':['黄桃','香蕉','西瓜','草莓'],\n",
    "               '数量':[13,40,63,25],\n",
    "               '价格':[2.5,2,11,5.8]})\n",
    "print(df)\n",
    "stack_df = df.stack() # 将数据从\"宽格式\"转换为\"长格式\"，结果是一个Series，具有多级索引（行索引和原列名）\n",
    "print(stack_df)\n",
    "print(stack_df.unstack())\n",
    "print(stack_df.unstack(level=0)) # 行和列会互换，与最初的数据相比"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.8 虚拟变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      专科     其他     博士     本科     硕士     高中\n",
      "0  False  False   True  False  False  False\n",
      "1  False  False  False  False   True  False\n",
      "2  False  False  False   True  False  False\n",
      "3   True  False  False  False  False  False\n",
      "4  False  False  False  False  False   True\n",
      "5  False   True  False  False  False  False\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "s = pd.Series(['博士','硕士','本科','专科','高中','其他'])\n",
    "print(pd.get_dummies(s))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.9 规范化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2         3\n",
      "0  6.677490  4.026739 -0.747324 -1.753904\n",
      "1  5.870536  5.738074  5.038395 -2.440585\n",
      "2  4.272972  3.768001  3.124917  5.159984\n",
      "3  0.742403  2.428106  4.225631  2.217492\n",
      "          0         1         2         3\n",
      "0  1.000000  0.482975  0.000000  0.090346\n",
      "1  0.864037  1.000000  1.000000  0.000000\n",
      "2  0.594864  0.404806  0.669276  1.000000\n",
      "3  0.000000  0.000000  0.859522  0.612859\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "df = pd.DataFrame(np.random.randn(4,4)* 4 + 3)\n",
    "print(df)\n",
    "# 最小-最大归一化（Min-Max Normalization）操作，将每一列的数据都转换到[0,1]区间内，(当前值 - 最小值) / (最大值 - 最小值)\n",
    "print(df.apply(lambda x: (x - np.min(x)) / (np.max(x) - np.min(x))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.10 连续属性离散化\n",
    "### 4.10.1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(18, 25], (18, 25], (18, 25], (25, 35], (18, 25], ..., (25, 35], (60, 100], (35, 60], (35, 60], (25, 35]]\n",
      "Length: 12\n",
      "Categories (4, interval[int64, right]): [(18, 25] < (25, 35] < (35, 60] < (60, 100]]\n",
      "cats的类型: <class 'pandas.core.arrays.categorical.Categorical'>\n",
      "[0 0 0 1 0 0 2 1 3 2 2 1] <class 'numpy.ndarray'>\n",
      "IntervalIndex([(18, 25], (25, 35], (35, 60], (60, 100]], dtype='interval[int64, right]') <class 'pandas.core.indexes.interval.IntervalIndex'>\n",
      "(18, 25]     5\n",
      "(25, 35]     3\n",
      "(35, 60]     3\n",
      "(60, 100]    1\n",
      "Name: count, dtype: int64\n",
      "-------\n",
      "[[18, 26), [18, 26), [18, 26), [26, 36), [18, 26), ..., [26, 36), [61, 100), [36, 61), [36, 61), [26, 36)]\n",
      "Length: 12\n",
      "Categories (4, interval[int64, left]): [[18, 26) < [26, 36) < [36, 61) < [61, 100)]\n",
      "-------\n",
      "['Youth', 'Youth', 'Youth', 'YoungAdult', 'Youth', ..., 'YoungAdult', 'Senior', 'MiddleAged', 'MiddleAged', 'YoungAdult']\n",
      "Length: 12\n",
      "Categories (4, object): ['Youth' < 'YoungAdult' < 'MiddleAged' < 'Senior']\n",
      "-------\n",
      "    ages      label\n",
      "0     20   (18, 25]\n",
      "1     22   (18, 25]\n",
      "2     25   (18, 25]\n",
      "3     27   (25, 35]\n",
      "4     21   (18, 25]\n",
      "5     23   (18, 25]\n",
      "6     37   (35, 60]\n",
      "7     31   (25, 35]\n",
      "8     61  (60, 100]\n",
      "9     45   (35, 60]\n",
      "10    41   (35, 60]\n",
      "11    32   (25, 35]\n",
      "ages\n",
      "(18, 25]     5\n",
      "(25, 35]     3\n",
      "(35, 60]     3\n",
      "(60, 100]    1\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "ages=[20,22,25,27,21,23,37,31,61,45,41,32]\n",
    "bins = [18,25,35,60,100] # 分箱边界\n",
    "cats = pd.cut(ages,bins)\n",
    "print(cats) # 显示每个年龄所属的区间\n",
    "print(\"cats的类型:\",type(cats)) # 显示cats的类型（Categorical）\n",
    "print(cats.codes, type(cats.codes)) # 显示编码（0,1,2,3）和类型\n",
    "print(cats.categories, type(cats.categories)) # 显示所有区间类别\n",
    "print(pd.Series(cats).value_counts())  # 统计每个区间的数量\n",
    "print('-------')\n",
    "print(pd.cut(ages,[18,26,36,61,100],right=False)) # right=False 表示区间包含左边界，不包含右边界\n",
    "print('-------')\n",
    "group_names=['Youth','YoungAdult','MiddleAged','Senior']\n",
    "print(pd.cut(ages,bins,labels=group_names))\n",
    "print('-------')\n",
    "df = pd.DataFrame({'ages':ages})\n",
    "group_names=['Youth','YoungAdult','MiddleAged','Senior']\n",
    "s = pd.cut(df['ages'],bins)  \n",
    "df['label'] = s\n",
    "cut_counts = s.value_counts(sort=False) \n",
    "print(df)\n",
    "print(cut_counts)\n",
    "plt.scatter(df.index,df['ages'],cmap = 'Reds',c = cats.codes) # 散点图\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.10.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    (-0.636, 0.0123]\n",
      "1      (0.658, 4.293]\n",
      "2    (-3.157, -0.636]\n",
      "3    (-3.157, -0.636]\n",
      "4     (0.0123, 0.658]\n",
      "dtype: category\n",
      "Categories (4, interval[float64, right]): [(-3.157, -0.636] < (-0.636, 0.0123] < (0.0123, 0.658] < (0.658, 4.293]]\n",
      "(-3.157, -0.636]    250\n",
      "(-0.636, 0.0123]    250\n",
      "(0.0123, 0.658]     250\n",
      "(0.658, 4.293]      250\n",
      "Name: count, dtype: int64\n",
      "------\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = np.random.randn(1000)\n",
    "s = pd.Series(data)\n",
    "cats = pd.qcut(s,4)  \n",
    "print(cats.head())\n",
    "print(pd.Series(cats).value_counts())\n",
    "print('------')\n",
    "plt.scatter(s.index,s,cmap = 'Greens',c = pd.qcut(data,4).codes)\n",
    "plt.xlim([0,1000])\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.11 随机采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 4 0 3 1]\n",
      "    0   1   2   3\n",
      "2   8   9  10  11\n",
      "4  16  17  18  19\n",
      "0   0   1   2   3\n",
      "3  12  13  14  15\n",
      "1   4   5   6   7\n",
      "    0   1   2   3\n",
      "3  12  13  14  15\n",
      "0   0   1   2   3\n",
      "4  16  17  18  19\n",
      "   0  1  2  3\n",
      "1  4  5  6  7\n",
      "0  0  1  2  3\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.DataFrame(np.arange(5*4).reshape(5,4))\n",
    "sampler = np.random.permutation(5)\n",
    "print(sampler)\n",
    "print(df.take(sampler))\n",
    "print(df.take(np.random.permutation(len(df))[:3]))\n",
    "print(df.sample(2))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python39",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.21"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
